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Firmness is an important fruit quality trait in northern highbush blueberry (Vaccinium corymbosum). Many researchers, growers, and packers rely on machines for measuring firmness right after harvest and during postharvest cold storage of fresh fruit. In this study, we compared two machines that use compression firmness measurements to determine a force-deformation value. The first firmness-testing machine has been in use for the past 30 years by blueberry (Vaccinium) researchers and packers worldwide. The second has been on the market for the past 5 years. We compared fruit firmness and size measurements for several commercial cultivars and breeding accessions of northern highbush blueberry by both machines at harvest and 2 weeks postharvest. In general, we found there were slight differences in fruit firmness and size measurements between the two machines, but these measurements were generally consistent across the machines. Our study suggests that, in general, one machine can predict the measurements taken on the other machine.
Breeding programs around the world continually collect data on large numbers of individuals. To be able to combine data collected across regions, years, and experiments, research communities develop standard operating procedures for data collection and measurement. One such method is a crop ontology, or a standardized vocabulary for collecting data on commonly measured traits. The ontology is also computer readable to facilitate the use of data management systems such as databases. Blueberry breeders and researchers across the United States have come together to develop the first standardized crop ontology in blueberry (Vaccinium spp.). We provide an overview and report on the construction of the first blueberry crop ontology and the 178 traits and methods included within. Researchers of Vaccinium species—such as other blueberry species, cranberry, lingonberry, and bilberry—can use the described crop ontology to collect phenotypic data of greater quality and consistency, interoperability, and computer readability. Crop ontologies, as a shared data language, benefit the entire worldwide research community by enabling collaborative meta-analyses that can be used with genomic data for quantitative trait loci, genome-wide association studies, and genomic selection analysis.